Effective web-based clinical practice guidelines resources: recommendations from a mixed methods usability study
Bibliographic record
Abstract
BACKGROUND: Clinical practice guidelines (CPG) are an important knowledge translation resource to help clinicians stay up to date about relevant clinical knowledge. Effective communication of guidelines, including format, facilitates its implementation. Despite the digitalization of healthcare, there is little literature to guide CPG website creation for effective dissemination and implementation. Our aim was to assess the effectiveness of the content and format of the Diabetes Canada CPG website, and use our results to inform recommendations for other CPG websites. METHODS: Fourteen clinicians (family physicians, nurses, pharmacists, and dieticians) in diabetes care across Canada participated in this mixed-methods study (questionnaires, usability testing and interviews). Participants "thought-aloud" while completing eight usability tasks on the CPG website. Outcomes included task success rate, completion time, click per tasks, resource used, paths, search attempts and success rate, and error types. Participants were then interviewed. RESULTS: The Diabetes Canada CPG website was found to be usable. Participants had a high task success rate of 79% for all tasks and used 144 (standard deviation (SD) = 152) seconds and 4.6 (SD = 3.9) clicks per task. Interactive tools were most frequently used compared to full guidelines and static tools. Misinterpretation accounted for 48% of usability errors. Participants overall found the website intuitive, with effective content and design elements. CONCLUSION: Different versions of CPG information (e.g. interactive tools, quick reference guide, static tools) can help answer clinical questions more quickly. Effective web design should be assessed during CPG website creation for effective guideline dissemination and implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.338 | 0.394 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".